CVE Vulnerabilities

CVE-2026-73072

Heap-based Buffer Overflow

Published: Aug 11, 2026 | Modified: Oct 06, 2026
CVSS 3.x
7.3
HIGH
Source:
NVD
CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H
CVSS 2.x
RedHat/V2
RedHat/V3
7.8 IMPORTANT
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Ubuntu
MEDIUM
root.io logo minimus.io logo echo.ai logo

Vim is an open source, command line text editor. Prior to 9.2.0846, set_sofo() in src/spellfile.c reuses sl_sal_first[] without resetting values left by set_sal_first(), so a crafted spell file containing an SN_SAL section before an SN_SOFO section causes under-counted mapping lists and attacker-influenced writes beyond a heap allocation. This issue is fixed in version 9.2.0846.

Weakness

A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc().

Affected Software

NameVendorStart VersionEnd Version
VimVim*9.2.0846 (excluding)
Red Hat Enterprise Linux 10RedHatvim-2:9.1.083-9.el10_2.20*
Red Hat Enterprise Linux 7 Extended Lifecycle SupportRedHatvim-2:7.4.629-8.el7_9.2*
Red Hat Enterprise Linux 8RedHatvim-2:8.0.1763-31.el8_10.7*
Red Hat Enterprise Linux 8RedHatvim-2:8.0.1763-31.el8_10.7*
Red Hat Enterprise Linux 9RedHatvim-2:8.2.2637-26.el9_8.21*
Red Hat Enterprise Linux 9RedHatvim-2:8.2.2637-26.el9_8.21*
Red Hat Enterprise Linux 9.2 Update Services for SAP SolutionsRedHatvim-2:8.2.2637-20.el9_2.3*
Red Hat Enterprise Linux 9.4 Update Services for SAP SolutionsRedHatvim-2:8.2.2637-20.el9_4.4*
Red Hat Enterprise Linux 9.6 Extended Update SupportRedHatvim-2:8.2.2637-22.el9_6.4*
Red Hat AI Inference Server 3.2RedHatrhaiis/model-opt-cuda-rhel9:1790621714*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-cuda-rhel9:1790621718*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-rocm-rhel9:1790621713*
Red Hat AI Inference Server 3.5RedHatrhaii/vllm-rocm-rhel9:1791425869*
Red Hat AI Inference Server 3.5RedHatrhaii/vllm-gaudi-rhel9:1791427182*
Red Hat AI Inference Server 3.5RedHatrhaii/model-opt-cuda-rhel9:1791803821*
Red Hat AI Inference Server 3.5RedHatrhaii/vllm-cuda-rhel9:1791473631*
Red Hat AI Inference Server 3.5RedHatrhaii/vllm-spyre-rhel9:1791818357*
Red Hat AI Inference Server 3.5RedHatrhaii/vllm-cpu-rhel9:1791473614*
Red Hat OpenShift AI 3.0RedHatrhai/base-image-rocm-rhel9:1790276886*
Red Hat OpenShift AI 3.0RedHatrhai/base-image-tpu-rhel9:1790276884*
Red Hat OpenShift AI 3.0RedHatrhai/base-image-cpu-rhel9:1790277045*
Red Hat OpenShift AI 3.0RedHatrhai/base-image-spyre-rhel9:1790276889*
Red Hat OpenShift AI 3.0RedHatrhai/base-image-cuda-rhel9:1790276974*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-tpu-rhel9:1790703497*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-rocm-rhel9:1790703506*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-cpu-rhel9:1790703590*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-spyre-rhel9:1790703568*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-cuda-rhel9:1790703586*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-rocm-rhel9:1790703494*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cpu-rhel9:1790703615*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-tpu-rhel9:1790703516*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-rocm-6.4-rhel9:1790703524*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-13.0-rhel9:1790703601*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-rocm-7.0-rhel9:1790703516*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-12.9-rhel9:1790703608*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-spyre-rhel9:1790703579*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-neuron-rhel9:1790703516*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-13.0-rhel9:1791231820*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-12.9-rhel9:1791232257*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-neuron-rhel9:1790703542*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-tpu-rhel9:1790703539*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-gaudi-rhel9:1790703553*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-cpu-rhel9:1790703631*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-spyre-rhel9:1790703597*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-cuda-13.0-rhel9:1790703641*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-cuda-12.9-rhel9:1790703630*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-rocm-7.1-rhel9:1790703541*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-rocm-6.4-rhel9:1790703545*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-gaudi-rhel9:1790703552*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-cpu-rhel9:1790703656*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-neuron-rhel9:1790703557*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-rubin-rhel9:1790703707*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-cuda-13.0-rhel9:1790703645*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-tpu-rhel9:1790703554*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-rocm-7.14-rhel9:1790703554*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-spyre-rhel9:1790703630*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-cuda-12.9-rhel9:1790703706*
Red Hat Update Infrastructure 5RedHatrhui5/cds-kubernetes-rhel9:1789479916*
Red Hat Update Infrastructure 5RedHatrhui5/cds-rhel9:1789479900*
Red Hat Update Infrastructure 5RedHatrhui5/haproxy-rhel9:1789479891*
Red Hat Update Infrastructure 5RedHatrhui5/installer-rhel9:1789482961*
Red Hat Update Infrastructure 5RedHatrhui5/rhua-rhel9:1790242004*
VimUbuntuesm-infra-legacy/trusty*
VimUbuntuesm-infra-legacy/xenial*
VimUbuntuesm-infra/bionic*
VimUbuntuesm-infra/focal*
VimUbuntujammy*
VimUbuntunoble*
VimUbunturesolute*
VimUbuntuupstream*

Potential Mitigations

  • Use automatic buffer overflow detection mechanisms that are offered by certain compilers or compiler extensions. Examples include: the Microsoft Visual Studio /GS flag, Fedora/Red Hat FORTIFY_SOURCE GCC flag, StackGuard, and ProPolice, which provide various mechanisms including canary-based detection and range/index checking.
  • D3-SFCV (Stack Frame Canary Validation) from D3FEND [REF-1334] discusses canary-based detection in detail.
  • Run or compile the software using features or extensions that randomly arrange the positions of a program’s executable and libraries in memory. Because this makes the addresses unpredictable, it can prevent an attacker from reliably jumping to exploitable code.
  • Examples include Address Space Layout Randomization (ASLR) [REF-58] [REF-60] and Position-Independent Executables (PIE) [REF-64]. Imported modules may be similarly realigned if their default memory addresses conflict with other modules, in a process known as “rebasing” (for Windows) and “prelinking” (for Linux) [REF-1332] using randomly generated addresses. ASLR for libraries cannot be used in conjunction with prelink since it would require relocating the libraries at run-time, defeating the whole purpose of prelinking.
  • For more information on these techniques see D3-SAOR (Segment Address Offset Randomization) from D3FEND [REF-1335].

References